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Audit data analytics (ADA) allow auditors to examine entire populations of client data for anomalies (i.e., population testing). While population testing has the potential to enhance the quality of audits, little research exists regarding its implications on auditor liability. Accordingly, we conduct an experiment to examine the possibility that population testing creates significant litigation exposure when materially misstated transactions are flagged as anomalies but not selected for further testing (a “ticking time bomb” according to an interviewed audit practitioner). Combining theory on counterfactual reasoning and persuasion, we predict that when the audit approach is framed as data-driven, jurors will assess higher auditor negligence when misstatements were initially flagged as anomalies by the ADA (vs. not flagged), but this effect is reduced when the audit approach is framed as risk-based. Results are stronger than expected as framing the audit approach as risk-based not only reduces but eliminates the litigation exposure associated with flagged but not tested transactions (i.e., defuses the time bomb that manifests with data-driven framing). Cumulatively, our study demonstrates how the description of the audit approach influences jurors’ auditor negligence assessments, and the litigation exposure associated with ADA. These results have important implications for audit practice and regulation, future research, and the legal system.
Blake Holman, University of Kentucky
Jenny Ulla, University of Nevada - Las Vegas
Jonathan H Grenier, Miami University
D. Jordan Lowe, Arizona State University - Tempe